scholarly journals Genetic variation in Japanese Holstein cattle for EBL development

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Yasuko Inagaki ◽  
Tomoko Kobayashi ◽  
Yoshihito Suda ◽  
Kazuya Kusama ◽  
Kazuhiko Imakawa
Keyword(s):  
Large Scale ◽  
Leukemia Virus ◽  
Tumor Development ◽  
Epidemiological Data ◽  
Pedigree Information ◽  
Holstein Cattle ◽  
Large Scale Data ◽  
Bovine Leukosis ◽  
Inspection Data ◽  
Scale Data

Abstract Background Infection with bovine leukemia virus (BLV), the causative agent for enzootic bovine leukosis (EBL), is increasing in dairy farms of Japan. The tendency of tumor development following BLV infection in certain cow families and bull lines has previously been described. We therefore hypothesized the existence of a genetic component which differentiates cattle susceptibility to the disease. Results We analyzed routinely collected large-scale data including postmortem inspection data, which were combined with pedigree information and epidemiological data of BLV infection. A total of 6,022 postmortem inspection records of Holstein cattle, raised on 226 farms served by a regional abattoir over 10 years from 2004 to 2015, were analyzed for associations between sire information and EBL development. We then identified statistically the relative susceptibility to EBL development for the progeny of specific sires and paternal grandsires (PGSs). The heritability of EBL development was calculated as 0.19. Similarly, proviral loads (PVLs) of progeny from identified sires and PGSs were analyzed, but no significant differences were found. Conclusions These observations suggest that because EBL development in our Holstein population is, at least in part, influenced by genetic factors independent of PVL levels, genetic improvement for lower incidence of EBL development in cattle notwithstanding BLV infection is possible.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

COMMUNITY-CURATED DATA RESOURCES AND LARGE-SCALE DATA-MODEL SYNTHESES: THE CHILDREN OF COHMAP

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Local and global approaches of affinity propagation clustering for large scale data

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang
Keyword(s):  
Large Scale ◽  
Affinity Propagation ◽  
Large Scale Data ◽  
Affinity Propagation Clustering ◽  
Scale Data

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

On Workload-Aware DRAM Failure Prediction in Large-Scale Data Centers

2021 ◽  
Author(s):  
Xingyi Wang ◽  
Yu Li ◽  
Yiquan Chen ◽  
Shiwen Wang ◽  
Yin Du ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Failure Prediction ◽  
Large Scale Data ◽  
Scale Data
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